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  • The subspace identification methods can further benefit from the prior information incorporation algorithm proposed in this paper. In the industrial environment, there is often some knowledge about the identified system, which can be used to improve the model quality and its compliance with first principles. The proposed algorithm has two stages. The first one is similar to the subspace methods as it uses their interpretation as an optimization problem of finding parameters of an optimal multi-step linear predictor for the experimental data. The second stage with state space model realization from the posterior impulse response estimate is different from the standard subspace methods as it is based on the SWLR (structured weighted lower rank) approximation, which is necessary to preserve the prior information incorporated in the first stage.
  • The subspace identification methods can further benefit from the prior information incorporation algorithm proposed in this paper. In the industrial environment, there is often some knowledge about the identified system, which can be used to improve the model quality and its compliance with first principles. The proposed algorithm has two stages. The first one is similar to the subspace methods as it uses their interpretation as an optimization problem of finding parameters of an optimal multi-step linear predictor for the experimental data. The second stage with state space model realization from the posterior impulse response estimate is different from the standard subspace methods as it is based on the SWLR (structured weighted lower rank) approximation, which is necessary to preserve the prior information incorporated in the first stage. (en)
  • Metody %22subspace identification%22 mohou být dále vylepšeny zahrnutím apriorní informace. V průmyslových aplikacích je obvykle dostupná apriorní znalost, která může zlepšit kvalitu modeluu i jeho soulad s fyzikálními princopy. Navržený algoritmus má dva kroky: první krok je podobný metodám %22subspace identification%22 formulovaným jako optimální vícekrokový prediktor. Ve druhém kroku se hledá stavová realizace na základě impulsní odezvy tak, aby se zachovalala apriorní informace z kroku 1, metodou SWLR (structured weighted lower rank) aproximace. (cs)
Title
  • Subspace like identification incorporating prior information
  • Identificace s využitím apriorní informace (cs)
  • Subspace like identification incorporating prior information (en)
skos:prefLabel
  • Subspace like identification incorporating prior information
  • Identificace s využitím apriorní informace (cs)
  • Subspace like identification incorporating prior information (en)
skos:notation
  • RIV/68407700:21230/09:03148020!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/05/2075), P(GA102/08/0442), Z(MSM6840770038)
http://linked.open...iv/cisloPeriodika
  • 4
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 344629
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/09:03148020
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Grey-box models; Prior information; State-space realization; Subspace methods; System identification (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • GB - Spojené království Velké Británie a Severního Irska
http://linked.open...ontrolniKodProRIV
  • [5DC535A09801]
http://linked.open...i/riv/nazevZdroje
  • Automatica
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 45
http://linked.open...iv/tvurceVysledku
  • Havlena, Vladimír
  • Trnka, Pavel
http://linked.open...ain/vavai/riv/wos
  • 000265155700030
http://linked.open...n/vavai/riv/zamer
issn
  • 0005-1098
number of pages
http://localhost/t...ganizacniJednotka
  • 21230
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